1 Need for a good control

  • A good control group is crucial.

  • To assess the effect of an intervention we need to compare a test and control group

  • This is often not possible in a pretest/posttest design: e.g. effect before and after administring a drug without the use of a placebo group

  • Groups in an observational study are often not comparable: advanced statistical methods are required to draw causal conclusions.

  • Double blinding

  • We have to be aware of confounding!

  • Randomized studies: random assignment of subjects in the study to the different treatment arms \(\rightarrow\) comparable groups.

2 Randomisation

  • Randomisation completely at random (no systematic allocation)

2.1 Simple randomisation

  • Can lead to differences in the number of experimental units in each treatment arm

  • in 5% of the cases we might observe an imbalance of
    • of at least 60:40 in a study with 100 subjects
    • of at least 531:469 in a study with 1000 subjects
  • This imbalance is not problematic but causes a loss in precision.

2.2 Balanced randomisation

  • Equal numbers of each treatment are assigned to a block of 2 or 4 patients.
      1. AB, (2) BA
      1. AABB, (2) ABAB, (3) ABBA, (4) BABA, (5) BAAB, (6) BBAA
  • Gebalanced randomisation ensures \(\pm\) the same number of people in the control and the treatment arm of the experiment.

  • Does not make that we have an equal number of males with and without the treatment, etc.

  • In small studies it is possible that the groups are inbalanced in other characteristics (e.g. gender, race, age …)

  • This is not problematic because it occurs at random, but, again it causes a loss in precision.

2.3 Stratified randomization**

  • The imbalance according to for instance gender can be avoided using stratified randomisation: balanced randomisatie per stratum
Stratified Randomisatie

Stratified Randomisatie

3 Blocking

3.1 Gene expression example

  • dm: diabetic medium, nd: non diabetic medium, co: control
  • 4 bio-reps, 2 techreps/biorep

  • dm: diabetic medium, nd: non diabetic medium, co: control
  • 4 bio-reps, 2 techreps/biorep, 2 plates A & B
  • treatment and plate almost entirely confounded

3.2 Nature methods: Points of significance - Blocking

4 Sample size

  • The sample size and the design are crucial

  • The larger the sample size the more precise the results

5 Wrap-up

  • Sample size is very important

  • To assess the effect of a treatment we should compare comparable and representative groups of subjects with and without the treatment (a good control!).

  • In observational studies the researcher cannot choose the treatment. It was the patient or their MD who had chosen it

  • In experimental studies the researcher assigns the treatment

  • Confounding can be avoided via randomisation

  • We can also correct for confounding in the statistical analysis for the confounders that have been registered.

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